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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Probing cellulose structures with vibrational spectroscopy

This paper reviews principles, data interpretations, and applications of vibrational spectroscopic methods used for analysis of cellulose in the isolated state and in plant cell walls or lignocellulose biomass. The paper begins with reviewing the crystalline structures of crystalline cellulose polymorphs and the principles of three different vibrational spectroscopy methods—infrared (IR), Raman, and sum frequency generation (SFG)—complemented with density functional theory calculations. Then, it discusses the vibrational modes of crystalline celluloses, how the chain orientation in crystalline domain is analyzed in each method, and how the concentration and spatial distribution of crystalline cellulose domains interspersed in amorphous matrices are manifested or analyzed differently in these three methods. Finally, the paper discusses examples of analyzing crystalline cellulose in plant cell walls or lignocellulose biomass with IR, Raman, and SFG including spectroscopic imaging. One review cannot cover all vibrational spectroscopy literatures on cellulose; this review aims at providing tutorial information, using selected literatures and experimental data, needed to interpret nano-, meso-, and micro-scale structures of cellulose in plant cell walls and lignocellulose biomass.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Review of computational approaches to predict the thermodynamic stability of inorganic solids

Improvements in the efficiency and availability of quantum chemistry codes, supercomputing centers, and open materials databases have transformed the accessibility of computational materials design approaches. Thermodynamic stability predictions play a central role in the efficacy of these approaches and should be considered carefully. This review covers the fundamentals of calculating thermodynamic stability using first-principles methods. Stability is delineated into two main topics — stability with respect to decomposition into competing phases and stability with respect to phase transition into alternative structures at fixed composition. For each topic, a summary of the state-of-the-art is provided along with a tutorial overview of practical considerations. The application of machine learning to both kinds of stability predictions is also covered. Finally, the limitations of thermodynamic stability predictions are discussed within the context of predicting the synthesizability of materials.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Green's function-stochastic methods framework for probing nonlinear evolution problems: Burger's equation, the nonlinear Schroedinger's equation, and hydrodynamic organization of near-molecular-scale vorticity

Research Highlights: > Systematic approach for physically probing nonlinear and random evolution problems. > Evolution of vortex sheets corresponds to evolution of an Ornstein-Uhlenbeck process. > Organization of near-molecular scale vorticity mediated by hydrodynamic modes. > Framework allows calculation of vorticity evolution within random strain fields. - Abstract: A framework which combines Green's function (GF) methods and techniques from the theory of stochastic processes is proposed for tackling nonlinear evolution problems. The framework, established by a series of easy-to-derive equivalences between Green's function and stochastic representative solutions of linear drift-diffusion problems, provides a flexible structure within which nonlinear evolution problems can be analyzed and physically probed. As a preliminary test bed, two canonical, nonlinear evolution problems - Burgers' equation and the nonlinear Schroedinger's equation - are first treated. In the first case, the framework provides a rigorous, probabilistic derivation of the well known Cole-Hopf ansatz. Likewise, in the second, the machinery allows systematic recovery of a known soliton solution. The framework is then applied to a fairly extensive exploration of physical features underlying evolution of randomly stretched and advected Burger's vortex sheets. Here, the governing vorticity equation corresponds to the Fokker-Planck equation of an Ornstein-Uhlenbeck process, a correspondence that motivates an investigation of sub-sheet vorticity evolution and organization. Under the assumption that weak hydrodynamic fluctuations organize disordered, near-molecular-scale, sub-sheet vorticity, it is shown that these modes consist of two weakly damped counter-propagating cross-sheet acoustic modes, a diffusive cross-sheet shear mode, and a diffusive cross-sheet entropy mode. Once a consistent picture of in-sheet vorticity evolution is established, a number of analytical results, describing the motion and spread of single, multiple, and continuous sets of Burger's vortex sheets, evolving within deterministic and random strain rate fields, under both viscous and inviscid conditions, are obtained. In order to promote application to other nonlinear problems, a tutorial development of the framework is presented. Likewise, time-incremental solution approaches and construction of approximate, though otherwise difficult-to-obtain backward-time GF's (useful in solution of forward-time evolution problems) are discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Introduction to the special section on learning and security for multi-agent systems

This report features six articles that provide an overview of the emerging research topics at the intersection learning, security, and multi-agent systems. Recent years have witnessed a surge in the number of works at their intersections, and they have appeared in system and control communities as well as many other communities in artificial intelligence, cyber–physical systems, and economics. The articles in this special section give accessible and comprehensive tutorials and surveys for a broad systems and control audience, covering topics including adversarial machine learning, multi-agent reinforcement learning, cyber resilience, resilient control systems, and game design. It is hopeful that this special section will spawn future interest and cross-disciplinary collaborations in this emerging transdisciplinary research area.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Benchmarking ADMM in Nonconvex NLPs

Here, we study connections between the alternating direction method of multipliers (ADMM), the classical method of multipliers (MM), and progressive hedging (PH). The connections are used to derive benchmark metrics and strategies to monitor and accelerate convergence and to help explain why ADMM and PH are capable of solving complex nonconvex NLPs. Specifically, we observe that ADMM is an inexact version of MM and approaches its performance when multiple coordination steps are performed. In addition, we use the observation that PH is a specialization of ADMM and borrow Lyapunov function and primal-dual feasibility metrics used in ADMM to explain why PH is capable of solving nonconvex NLPs. This analysis also highlights that specialized PH schemes can be derived to tackle a wider range of stochastic programs and even other problem classes. Our exposition is tutorial in nature and seeks to to motivate algorithmic improvements and new decomposition strategies.

42 ENGINEERING↗
DOE OSTI2023

Dynamic modeling and simulation of pressure swing adsorption processes using toPSAil

Pressure swing adsorption (PSA) has attracted significant recent interest for chemical process intensification due to its potential for high energy efficiency and amenability to small, modular designs. However, the lack of simulation tools that are readily available, transparent, and trusted, has been identified as a serious impediment to widespread adoption of PSA, as well as to further research on PSA modeling, numerical solution, optimization, and control. This paper presents a complete framework for dynamic modeling and simulation of PSA processes and its implementation in an open-source simulator called toPSAil. Further, the presentation is tutorial and includes many modeling and implementation details often overlooked in existing literature. Novel methods are presented for handling flow reversals and implementing various pressure–flow relationships, along with controlled boundary conditions. Finally, the code contains several innovations designed to improve efficiency and reduce the extensive trial-and-error tuning often required to produce a working PSA cycle.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Exploring MDSplus data-acquisition software and custom devices

MDSplus is a software tool designed for data acquisition, storage, and analysis of complex scientific experiments. Over the years, MDSplus has primarily been used for data management for fusion experiments. This paper demonstrates that MDSplus can be used for a much wider variety of systems and experiments. We present a step-by-step tutorial describing how to create a simple experiment, manage the data, and analyze it using MDSplus and Python. To this end, a custom example device was developed to be used as the data source. This device was built on an open-source electronic hardware platform, and it consists of a microcontroller and two sensors. Finally, we read data from these sensors, store it in MDSplus, and use JupyterLab to visualize and process it.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2012

Contouring Variability of the Penile Bulb on CT Images: Quantitative Assessment Using a Generalized Concordance Index

Purpose: Within a multicenter study (DUE-01) focused on the search of predictors of erectile dysfunction and urinary toxicity after radiotherapy for prostate cancer, a dummy run exercise on penile bulb (PB) contouring on computed tomography (CT) images was carried out. The aim of this study was to quantitatively assess interobserver contouring variability by the application of the generalized DICE index. Methods and Materials: Fifteen physicians from different Institutes drew the PB on CT images of 10 patients. The spread of DICE values was used to objectively select those observers who significantly disagreed with the others. The analyses were performed with a dedicated module in the VODCA software package. Results: DICE values were found to significantly change among observers and patients. The mean DICE value was 0.67, ranging between 0.43 and 0.80. The statistics of DICE coefficients identified 4 of 15 observers who systematically showed a value below the average (p value range, 0.013 - 0.059): Mean DICE values were 0.62 for the 4 'bad' observers compared to 0.69 of the 11 'good' observers. For all bad observers, the main cause of the disagreement was identified. Average DICE values were significantly worse from the average in 2 of 10 patients (0.60 vs. 0.70, p < 0.05) because of the limited visibility of the PB. Excluding the bad observers and the 'bad' patients,' the mean DICE value increased from 0.67 to 0.70; interobserver variability, expressed in terms of standard deviation of DICE spread, was also reduced. Conclusions: The obtained values of DICE around 0.7 shows an acceptable agreement, considered the small dimension of the PB. Additional strategies to improve this agreement are under consideration and include an additional tutorial of the so-called bad observers with a recontouring procedure, or the recontouring by a single observer of the PB for all patients included in the DUE-01 study.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons

Neural networks (NNs) are currently changing the computational paradigm on how to combine data with mathematical laws in physics and engineering in a profound way, tackling challenging inverse and ill-posed problems not solvable with traditional methods. However, quantifying errors and uncertainties in NN-based inference is more complicated than in traditional methods. This is because in addition to aleatoric uncertainty associated with noisy data, there is also uncertainty due to limited data, but also due to NN hyperparameters, overparametrization, optimization and sampling errors as well as model misspecification. Although there are some recent works on uncertainty quantification (UQ) in NNs, there is no systematic investigation of suitable methods towards quantifying the total uncertainty effectively and efficiently even for function approximation, and there is even less work on solving partial differential equations and learning operator mappings between infinite-dimensional function spaces using NNs. In this work, we present a comprehensive framework that includes uncertainty modeling, new and existing solution methods, as well as evaluation metrics and post-hoc improvement approaches. Further, to demonstrate the applicability and reliability of our framework, we present an extensive comparative study in which various methods are tested on prototype problems, including problems with mixed input-output data, and stochastic problems in high dimensions. In the Appendix, we include a comprehensive description of all the UQ methods employed. Further, to help facilitate the deployment of UQ in Scientific Machine Learning research and practice, we present and develop in [1] an open-source Python library (github.com/Crunch-UQ4MI/neuraluq), termed NeuralUQ, that is accompanied by an educational tutorial and additional computational experiments.

11 physics-informed neural networks↗
DOE OSTI2010

X-ray microtomography

In this tutorial, we describe X-ray microtomography as a technique to nondestructively characterize material microstructure in three dimensions at a micron level spatial resolution. While commercially available laboratory scale instrumentation is available, we focus our attention on synchrotron-based systems, where we can exploit a high flux, monochromatic X-ray beam to produce high fidelity three-dimensional images. A brief description of the physics and the mathematical analysis behind the technique is followed by example applications to specific materials characterization problems, with a particular focus on the utilization of three-dimensional image processing that can be used to extract a wide range of useful information.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Application of EELS in Materials Science

Electron energy-loss spectroscopy (EELS) in the transmission electron microscope (TEM) has become widely used for the analysis of the composition and electronic structure of materials at the nanoscale. This tutorial review provides an overview of the theory and applications of the technique and a few examples are provided to illustrate the type of information available. Some of the recent developments and future prospects of EELS are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2015

The TriBeam system: Femtosecond laser ablation in situ SEM

Femtosecond laser ablation offers the unique ability to remove material at rates that are orders of magnitude faster than existing ion beam technologies with little or no associated damage. By combining ultrafast lasers with state-of-the-art electron microscopy equipment, we have developed a TriBeam system capable of targeted, in-situ tomography providing chemical, structural, and topographical information in three dimensions of near mm{sup 3} sized volumes. The origins, development, physics, current uses, and future potential for the TriBeam system are described in this tutorial review. - Graphical abstract: Display Omitted - Highlights: • An emerging tool, the TriBeam, for in situ femtosecond (fs) laser ablation is presented. • Fs laser ablation aided tomography at the mm{sup 3}-scale is demonstrated. • Fs laser induced deposition of Pt is demonstrated at sub-diffraction limit resolution. • Fs laser surface structuring is reviewed as well as micromachining applications.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Microstructure evolution and mechanical properties of magnesium alloys containing long period stacking ordered phase

Magnesium alloys containing rare earth (RE) in conjunction with other alloying elements, such as Zn, have drawn increasing interest due to their high strength, high ductility and high creep-resistance with the existence of long period stacking ordered (LPSO) phase. Although the LPSO phase has been studied over a decade, the question remains how it impacts the mechanical properties of Mg-RE-X alloys, where X represents a metallic element with its atomic radius smaller than Mg. This paper provides a tutorial review of the current status on the role of LPSO phase in manipulating the mechanical properties in Mg-RE-X based alloys, where some outstanding issues are also highlighted and discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Thermal conductivity modeling of hybrid organic-inorganic crystals and superlattices

Hybrid organic-inorganic materials have attracted intensive research interests due to the superb physical properties that take advantage of both organic and inorganic components and their promising applications in functional devices such as light emitting diodes, solar cells, and flexible thermoelectrics. Understanding thermal transport in hybrid materials is of importance for both the reliability and the performance of the hybrid material-based systems. The organic-inorganic hybrid materials can be classified as nanocomposites, superlattices and crystals depending on the strength of organic-inorganic bonding and the feature size of organic and inorganic components. Previous research on thermal transport mainly focus on hybrid nanocomposites where phonon-interface scattering dominates the thermal transport process. However, in hybrid superlattices and crystals, organic and inorganic components are blended at the atomic scale by chemical bonds without a clear interface, and the phonon transport physics is not well understood. This article reviews the recent progress in ab-initio modeling on phonon transport in hybrid crystals and superlattices. The predicted thermal conductivity of II–VI based organic-inorganic semiconductors, organometal perovskites and organic-intercalated TiS2 superlattices are reviewed and compared with experimental data when available. This review provides guidance for modeling thermal conductivity of materials with complex atomic structures from the first principles, which could be a good tutorial for both graduate students and experienced researchers who are interested in thermal energy transport in emerging hybrid materials.

Chemistry↗
DOE OSTI2022

A primer on artificial intelligence in plant digital phenomics: embarking on the data to insights journey

Artificial intelligence (AI) has emerged as a fundamental component of global agricultural research that is poised to impact on many aspects of plant science. In digital phenomics, AI is capable of learning intricate structure and patterns in large datasets. We provide a perspective and primer on AI applications to phenome research. We propose a novel human-centric explainable AI (X-AI) system architecture consisting of data architecture, technology infrastructure, and AI architecture design. We clarify the difference between post hoc models and 'interpretable by design' models. We include guidance for effectively using an interpretable by design model in phenomic analysis. We also provide directions to sources of tools and resources for making data analytics increasingly accessible. In conclusion, this primer is accompanied by an interactive online tutorial.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Sub-Sampled Imaging for STEM: Maximising Image Speed, Resolution and Precision Through Reconstruction Parameter Refinement

Sub-sampling during image acquisition in scanning transmission electron microscopy (STEM) has been shown to provide a means to increase the overall speed of acquisition while at the same time providing an efficient means to control the dose, dose rate and dose overlap delivered to the sample. In this paper, we discuss specifically the parameters used to reconstruct sub-sampled images and highlight their effect on inpainting using the beta-process factor analysis (BPFA) methodology. The selection of the main control parameters can have a significant effect on the resolution, precision and sensitivity of the final inpainted images, and here we demonstrate a method by which these parameters can be optimised for any image in STEM. As part of this paper, we also provide a link to open source code and a tutorial on its use, whereby these parameters can be tested for any datasets. When coupled with the hardware necessary to rapidly sub-sample images in STEM, this approach can have significant implications for imaging beam sensitive materials and dynamic processes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Practical gyrokinetics

We report thousands of gyrokinetic papers have been published since the introduction of the gyrokinetic change of variables. The intent here is not to review the field, but rather the goal is to present a tutorial providing insight into why gyrokinetics is an appropriate description of turbulent transport. The focus is on turbulent transport in axisymmetric tokamaks, but many of the ideas and techniques are applicable to stellarators and other magnetic fusion devices with nested surfaces of constant pressure. Besides the origins of gyrokinetics and recent insights, gyrokinetic orderings and gyrokinetic variables are summarized. Then a compact, but careful, derivation of the simplest electrostatic gyrokinetic equation for tokamaks is presented, along with a brief mention of gyrokinetics for stellarators. The advantages of assuming scale separation between the finer spatial scales and faster time variation of the turbulence and the global behaviour and slow temporal evolution of the background are stressed. Moreover, the procedure for removing the adiabatic or Maxwell–Boltzmann response is emphasized. Scale separation allows the near Maxwellian behaviour of the background and the rapid variation of the turbulence to be described by separate, local gyrokinetic equations. The turbulent fluctuations are found by solving the nonlinear gyrokinetic equation for the non-adiabatic portion of the fluctuating distribution function. Also, generalizations of the gyrokinetic equation so that it retains electromagnetic and flow modifications are considered in detail along with some symmetry properties. In addition, ambipolarity, particle transport and heat transport are addressed, along with a discussion of the complications associated with describing momentum transport and profile evolution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

What's New in GSAS-II

The General Structure and Analysis Software II (GSAS-II) package is an all-new crystallographic analysis package written to replace and extend the capabilities of the universal and widely used GSAS and EXPGUI packages. GSAS-II was described in a 2013 article, but considerable work has been completed since then. This paper describes the advances, which include: rigid body fitting and structure solution modules; improved treatment for parametric refinements and equation of state fitting; and small-angle scattering data reduction and analysis. GSAS-II offers versatile and extensible modules for import and export of data and results. Capabilities are provided for users to select any version of the code. Code documentation has reached 150 pages and 17 web-tutorials are offered. © 2014 International Centre for Diffraction Data.

Toby, Brian H.↗